Machine Learning Engineer — AI Architecture Research
Pubblicato il 07-09-2026 - Jobgether in Italia
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Machine Learning Engineer — AI Architecture Research based in Italy.
This role focuses on researching and building next-generation AI model architectures that can move from experimental concepts to scalable production systems.
You will work at the intersection of machine learning research, model engineering, and real-world deployment.
The position offers the opportunity to challenge established architectural assumptions and explore alternatives to conventional Transformer-based designs.
You will design experiments, prototype new neural networks, and evaluate trade-offs across compute, memory, latency, and model performance.
The role involves close collaboration with inference and systems engineers to make research ideas efficient and deployable.
You will also contribute to research reproduction, benchmarking, technical exploration, and potentially open-source work.
This is an opportunity to have meaningful influence on AI architecture while working in a fast-moving, research-oriented environment.
Accountabilities:
- Research and develop novel neural network architectures, including alternatives or extensions to Transformers, recurrent and hybrid models, and long-context systems.
- Design and execute architecture-level experiments focused on scaling laws, memory mechanisms, training behavior, and compute-performance trade-offs.
- Prototype models end-to-end, translating research concepts into robust, training-ready implementations.
- Analyze model behavior, failure modes, inductive biases, and architectural strengths and limitations.
- Collaborate with inference and systems engineering teams to ensure new architectures are efficient, scalable, and suitable for deployment.
- Read, reproduce, evaluate, and extend cutting-edge machine learning research papers.
- Contribute to internal research notes, benchmarks, experiments, and open-source